All students are strongly encouraged to engage deeply in the course, interact with others, and actively participate (in person during class and asynchronously out of class) in all HS650 activities. Attending lectures regularly in person is important -- physical presence may be the most effective learning strategy. Lectures may be streamed and recorded only under special (uncommon) circumstances. Not all lectures will be recorded.

Tue, Aug 27, 2024: HS650 Syllabus and Course Organization, Homepage, Gradebook, Expectations HWs, Projects, DSPA Motivation
Thu, Aug 29, 2024: DSPA Chapter 1 (Introduction)
Tue, Sept 3: DSPA Chapter 1 (Introduction) + Chapter 1 (Intro) & Activity 1 (Text-to-Pixel Image)
Thu, Sept 5: DSPA Chapter 1 (Introduction) + Chapter 1 (Intro) & Activity 1 (Text-to-Pixel Image)
Tue, Sept 10: DSPA Chapter 2 (EDA & Viz), Activity 1(Text-to-Image)
Thu, Sept 12: DSPA Chapter 2 (EDA & Viz) & Imputation
Tue, Sept 17: DSPA Chapter 2 (EDA & Viz) Cohort Rebabancing & Activity 2 (2D/3D Histogram)
Thu, Sept 19: DSPA Chapter 2 (EDA & Viz) Mixture modeling and Chapter 3 (Linear Algebra)
Tue, Sept 24: DSPA Chapter 3 (Linear Algebra)
Thu, Sept 26: DSPA Chapter 3 (Linear Algebra) and motivation of Dimensionality Reduction (Chapter 4).
Tue, Oct 1: DSPA Chapter 4 (Linear and Non-Linear Dimensionality Reduction).
Thu, Oct 3: DSPA Chapter 4 (Non-Linear Dimensionality Reduction) and DSPA Chapter 5 (Supervised Classification).
Tue, Oct 8: DSPA Chapter 5 (Supervised Classification)
Thu, Oct 10: DSPA Chapter 5 (Supervised Classification) & Chapter 6 (Black-box ML - NN, SVM, RF)
Thu, Oct 17: DSPA Chapter 6 (Black-box ML - NN, SVM, RF) & Chapter 7 (Qualitative Data ML)
Tue, Oct 22: DSPA Chapter 7 (Qualitative Data ML) & Chapter 8 (Unsupervised ML/k-Means)
Thu, Oct 24: DSPA Chapter 8 (Unsupervised ML/k-Means) & Chapter 9 (Model Assessment)
Tue, Oct 29: DSPA Chapter 9 (Model Assessment) & Chapter 10 (Specialized Topics)
Thu, Oct 31: DSPA Chapter 10 (Specialized Topics) and Chapter 11 (Variable Selection)
Tue, Nov 5: Chapter 11 (Variable Selection & Knockoff Algorithm)
Thu, Nov 7: Chapter 12 (Large Longitudinal Data)
Tue, Nov 12: Chapter 13 (Optimization)
Thu, Nov 14: Chapter 13 (Optimization)
Tue, Nov 19: Chapter 13 (Optimization) and Chapter 14 (DNN)
Thu, Nov 21: Chapter 14 (DNN)
Tue, Nov 26: Chapter 14 (DNN)